The persistent feedback regarding the inaccuracy of option premiums, following the implementation of the new algorithm, prompted us to give a thorough explanation. As users began expressing concerns and even asked us to revert to the old engine, a critical question emerged: why had we made the switch? The decision to transition stemmed from two critical observations. First, specific strike prices demonstrated high volatility, rendering the last traded price unreliable for current market conditions and potentially leading to subsequent margin complications. Additionally, the revamped engine’s improved efficiency, achieved through a focused approach on pertinent securities, effectively mitigated concerns related to prolonged processing times.
Consider the following set of strike prices and their associated premiums (actual market data):
This table demonstrates the premiums associated with varying strike prices. Notably, the strike price of 24800, with an associated premium of 10, stands out as potentially problematic. Analyzing this data within the context of live trading dynamics reveals that while the premium might suggest a seemingly close match, the actual live market conditions suggest otherwise. Despite the premium appearing comparable to the requested value, a closer examination of the market movements and recent trade data indicates that securing this option at the indicated premium is highly unlikely. Our new algorithm take this into account to disregard 24800 as a valid strike price for premium 10. From the table we can see that it would actually be traded at a significantly higher premiums which we would assume as slippage and infact it’s not.
Our new option selection algorithm operates systematically and uses Black-Scholes model’s premiums as baseline premiums. To address the tendency of the Black-Scholes model to underestimate premiums in deep Out-of-The-Money (OTM) and In-The-Money (ITM) scenarios, we start by considering two strike prices and their premiums and use weighted average of implied volatility from them based on how far off their strike prices are from the considered strike price. Traversing the strike price ladder dynamically, we adjust the direction based on the requested premium. Employing a calculated weighted average implied volatility between these two strikes, we predict premiums for each position. This meticulous approach enables us to select strike prices that closely mirror the prevailing market sentiment, thus reducing discrepancies between back-test results and live trading outcomes.
While our algorithm may yield slightly inferior back-test results, its emphasis on precision and real-time market dynamics promises a more accurate representation of live trading scenarios. By combining the predictive power of the Black-Scholes model with an adaptive strike price selection mechanism, our algorithm empowers traders with a reliable and responsive tool, effectively navigating the intricacies of equity option trading with confidence and foresight. By addressing the fundamental issue of selecting infrequently traded strike prices, which may trade at significantly different prices than the last traded option, our algorithm ensures a more accurate reflection of market conditions, dispelling any misinterpretation of wrong strike price as slippage issues.